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A Monte Carlo EM algorithm for the estimation of a logistic auto-logistic model with missing data

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  • Marco Bee
  • Giuseppe Espa

Abstract

This paper proposes an algorithm for the estimation of the parameters of a Logistic Auto-logistic Model when some values of the target variable are missing at random but the auxiliary information is known for the same areas. First, we derive a Monte Carlo EM algorithm in the setup of maximum pseudo-likelihood estimation; given the analytical intractability of the conditional expectation of the complete pseudo-likelihood function, we implement the E-step by means of Monte Carlo simulation. Second, we give an example using a simulated dataset. Finally, a comparison with the standard non-missing data case shows that the algorithm gives consistent results.
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Suggested Citation

  • Marco Bee & Giuseppe Espa, 2008. "A Monte Carlo EM algorithm for the estimation of a logistic auto-logistic model with missing data," Letters in Spatial and Resource Sciences, Springer, vol. 1(1), pages 45-54, July.
    • Handle: RePEc:spr:lsprsc:v:1:y:2008:i:1:p:45-54
    DOI: 10.1007/s12076-008-0005-5
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    Cited by:

    1. Anping Chen & Marlon Boarnet & Mark Partridge & Raffaella Calabrese & Johan A. Elkink, 2014. "Estimators Of Binary Spatial Autoregressive Models: A Monte Carlo Study," Journal of Regional Science, Wiley Blackwell, vol. 54(4), pages 664-687, September.

    More about this item

    Keywords

    Spatial missing data; Monte Carlo EM algorithm; Logistic auto-logistic model; Pseudo-likelihood; C13; C15; C51;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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